LinkedIn Intelligence MCP Server

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context about using an 'unofficial API' (a WARNING), which informs about potential reliability or compliance issues. However, it doesn't describe authentication needs, rate limits, error conditions, or what specific details are returned beyond 'conversation ID'.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and front-loaded: the first sentence states the core purpose, followed by usage context, parameter explanation, return value, and a warning. Every sentence earns its place with no redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (single parameter, no annotations, but with an output schema), the description is reasonably complete. It covers purpose, usage, parameter semantics, and a behavioral warning. The presence of an output schema means it doesn't need to detail return values, but it could benefit from more behavioral context (e.g., error handling).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds meaningful semantics beyond the schema: it explains that 'profile_id' accepts 'LinkedIn profile ID or URN', clarifying the expected format. With 0% schema description coverage and only one parameter, this compensation is effective, though it doesn't provide examples or constraints.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('Get conversation ID and details') and resources ('for a specific profile'). It distinguishes itself from sibling tools like 'get_conversation' and 'get_conversations' by focusing on retrieving details for a specific profile rather than general conversation data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this tool ('Useful for finding the conversation ID to send a message to someone'), providing clear context for its application. It also implies an alternative workflow where this tool is a prerequisite for 'send_message', though it doesn't explicitly name alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.